Exploring covariates, rs-fMRI usability, and functional connectivity
Liwei Wang and MB Nebel
9/10/2021
#for flowchart
library(DiagrammeR)
#to export flowchart as .png
#webshot::install_phantomjs()
library(DiagrammeRsvg)
library(rsvg)
library(grid)
library(xtable)
library(dplyr)
library(ggplot2)
library(gridExtra)
#nest/unnest
library(tidyr)
#map function (kind of like a for loop)
library(purrr)
#tidy model summary
library(broom)
library(readxl)
#for tableby
library(arsenal)Define geom_split_violin()
Based on https://debruine.github.io/posts/plot-comparison/
GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, draw_group = function(self, data, ..., draw_quantiles = NULL){
data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x))
grp <- data[1,'group']
newdata <- plyr::arrange(transform(data, x = if(grp%%2==1) xminv else xmaxv), if(grp%%2==1) y else -y)
newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1, ])
newdata[c(1,nrow(newdata)-1,nrow(newdata)), 'x'] <- round(newdata[1, 'x'])
if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) {
stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <=
1))
quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles)
aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")), drop = FALSE]
aesthetics$alpha <- rep(1, nrow(quantiles))
both <- cbind(quantiles, aesthetics)
quantile_grob <- GeomPath$draw_panel(both, ...)
ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata, ...), quantile_grob))
}
else {
ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...))
}
})
geom_split_violin <- function (mapping = NULL, data = NULL, stat = "ydensity", position = "identity", ...,
draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE) {
layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin, position = position,
show.legend = show.legend, inherit.aes = inherit.aes,
params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...))
}Load Data
load('Data/DataWithPropensities_seed1.RData')
#convert PrimaryDiagnosis to factor
dat3$PrimaryDiagnosis <- factor(dat3$PrimaryDiagnosis, levels = c("Autism", "None"))
levels(dat3$PrimaryDiagnosis)## [1] "Autism" "None"dat3$PrimaryDiagnosis <- relevel(dat3$PrimaryDiagnosis, "None")
levels(dat3$PrimaryDiagnosis) = c("TD", "ASD")Reshape data to combine motion QC levels
#create dummy factor to include all subjects
dat3$noExclusion <- ifelse(dat3$ID>0, "Pass", "Pass")
dat3$noExclusion <- factor(dat3$noExclusion)
#convert KKI_criteria to factor level "Pass"
dat3$KKI_criteria <- factor(dat3$KKI_criteria, levels=c("Pass", "Fail"))
#convert Ciric_length to factor with reference level "Pass" to match KKI_criteria
dat3$Ciric_length <- factor(dat3$Ciric_length, levels=c("Pass", "Fail"))
#combine Ciric_length, KKI, and noExclusion exclusion into one variable
allVariables = c("ID", "PrimaryDiagnosis", "AgeAtScan", "Ciric_length", "KKI_criteria", "noExclusion",
"PANESS.TotalOverflowNotAccountingForAge", "SRS.Score", "WISC.GAI",
"DuPaulHome.InattentionRaw", "DuPaulHome.HyperactivityRaw", "ADOS.Comparable.Total",
"CurrentlyOnStimulants", "HeadCoil", "Sex",
"Diagnosis4Levels", "ADHD_Secondary", "SES.Family",
"Race2", "handedness", "CompletePredictorCases", "YearOfScan", "MeanFramewiseDisplacement.KKI")
idVariables = c("ID", "PrimaryDiagnosis", "AgeAtScan",
"PANESS.TotalOverflowNotAccountingForAge", "SRS.Score", "WISC.GAI",
"DuPaulHome.InattentionRaw", "DuPaulHome.HyperactivityRaw", "ADOS.Comparable.Total",
"CurrentlyOnStimulants", "HeadCoil", "Sex",
"Diagnosis4Levels", "ADHD_Secondary", "SES.Family",
"Race2", "handedness", "CompletePredictorCases", "YearOfScan", "MeanFramewiseDisplacement.KKI")
qcMelt <- reshape2::melt(dat3[, allVariables],
id.vars=names(dat3)[which(names(dat3) %in% idVariables)],
variable.name = "Motion.Exclusion.Level",
value.name = "Included")## Warning: attributes are not identical across measure variables; they will be
## dropped#rename exclusion levels
#NOTE: need None to be highest level for geom_split_violin
levels(qcMelt$Motion.Exclusion.Level) <- c("Strict", "Lenient", "None")
#convert Included to factor with pass as reference
qcMelt$Included <- factor(qcMelt$Included, levels=c("Pass", "Fail"))
#rename levels of value
levels(qcMelt$Included) <- c("Included", "Excluded")
with(qcMelt, table(PrimaryDiagnosis, Motion.Exclusion.Level, Included))## , , Included = Included
##
## Motion.Exclusion.Level
## PrimaryDiagnosis Strict Lenient None
## TD 151 308 372
## ASD 29 114 173
##
## , , Included = Excluded
##
## Motion.Exclusion.Level
## PrimaryDiagnosis Strict Lenient None
## TD 221 64 0
## ASD 144 59 0qcMelt$Diagnosis4Levels <- factor(qcMelt$Diagnosis4Levels)Check consistency of diagnosis variables created by Ben and Liwei
#limit to complete predictor cases
dat3 <- filter(dat3, CompletePredictorCases==1)
#convert Diagnosis4Levels to factor
dat3$Diagnosis4Levels <- factor(dat3$Diagnosis4Levels, levels = c("Autism", "Autism+ADHD", "None"))
table(dat3$Diagnosis4Levels)##
## Autism Autism+ADHD None
## 49 102 353table(dat3$ADHD_Secondary)##
## 0 1
## 402 10249 Autism + 353 None = 402 ADHD_Secondary=0
1. Impact of motion QC on sample size
tabN<- tableby(PrimaryDiagnosis~Motion.Exclusion.Level, data=filter(qcMelt, Included=="Included"))
summary(tabN,
title='N',digits=0, digits.p=4,digits.pct=1, numeric.simplify=TRUE, total=FALSE, test=FALSE)| TD (N=831) | ASD (N=316) | |
|---|---|---|
| Motion.Exclusion.Level | ||
| Â Â Â Strict | 151 (18.2%) | 29 (9.2%) |
| Â Â Â Lenient | 308 (37.1%) | 114 (36.1%) |
| Â Â Â None | 372 (44.8%) | 173 (54.7%) |
1b. Summarize sociodemographic info for complete cases for paper
#sex
qcMelt$Sex <- relevel(as.factor(qcMelt$Sex), "M")
tabSex<- tableby( PrimaryDiagnosis~Sex, data=filter(qcMelt, Motion.Exclusion.Level=="None" & Included=="Included"), control=tableby.control(numeric.test="kwt", cat.test="chisq", total=FALSE))
#age
tabAge<- tableby( PrimaryDiagnosis~AgeAtScan, data=filter(qcMelt, Motion.Exclusion.Level=="None" & Included=="Included"), control=tableby.control(numeric.test="kwt", cat.test="chisq", total=FALSE))
tab12 <- merge(tabSex, tabAge)
#Race
tabRace<- tableby( PrimaryDiagnosis~Race2, data=filter(qcMelt, Motion.Exclusion.Level=="None" & Included=="Included"), control=tableby.control(numeric.test="kwt", cat.test="fe", total=FALSE))
tab123 <- merge(tab12, tabRace)
#SES
tabSES<- tableby( PrimaryDiagnosis~SES.Family, data=filter(qcMelt, Motion.Exclusion.Level=="None" & Included=="Included"), control=tableby.control(numeric.test="kwt", cat.test="chisq", total=FALSE))
tab1234 <- merge(tab123, tabSES)
#Handedness
qcMelt$handedness <- factor(qcMelt$handedness)
qcMelt$handedness <- relevel(qcMelt$handedness, "Right")
tabHand<- tableby( PrimaryDiagnosis~handedness, data=filter(qcMelt, Motion.Exclusion.Level=="None" & Included=="Included"), control=tableby.control(numeric.test="kwt", cat.test="chisq", total=FALSE))
tab12345 <- merge(tab1234, tabHand)
#Currently on Stimulants
qcMelt$CurrentlyOnStimulants <- factor(qcMelt$CurrentlyOnStimulants)
tabStim<- tableby( PrimaryDiagnosis~CurrentlyOnStimulants, data=filter(qcMelt, Motion.Exclusion.Level=="None" & Included=="Included"), control=tableby.control(numeric.test="kwt", cat.test="chisq", total=FALSE))
tab123456 <- merge(tab12345, tabStim)
summary(tab123456)| TD (N=372) | ASD (N=173) | p value | |
|---|---|---|---|
| Sex | < 0.001 | ||
| Â Â Â M | 258 (69.4%) | 148 (85.5%) | |
| Â Â Â F | 114 (30.6%) | 25 (14.5%) | |
| AgeAtScan | 0.902 | ||
| Â Â Â Mean (SD) | 10.383 (1.246) | 10.382 (1.356) | |
| Â Â Â Range | 8.020 - 12.980 | 8.010 - 12.990 | |
| Race2 | 0.001 | ||
| Â Â Â N-Miss | 0 | 1 | |
| Â Â Â African American | 40 (10.8%) | 11 (6.4%) | |
| Â Â Â Asian | 28 (7.5%) | 3 (1.7%) | |
| Â Â Â Biracial | 47 (12.6%) | 14 (8.1%) | |
| Â Â Â Caucasian | 257 (69.1%) | 144 (83.7%) | |
| SES.Family | < 0.001 | ||
| Â Â Â N-Miss | 8 | 9 | |
| Â Â Â Mean (SD) | 54.243 (9.371) | 51.396 (9.506) | |
| Â Â Â Range | 18.500 - 66.000 | 27.000 - 66.000 | |
| handedness | 0.259 | ||
| Â Â Â N-Miss | 1 | 0 | |
| Â Â Â Right | 333 (89.8%) | 147 (85.0%) | |
| Â Â Â Left | 19 (5.1%) | 12 (6.9%) | |
| Â Â Â Mixed | 19 (5.1%) | 14 (8.1%) | |
| CurrentlyOnStimulants | < 0.001 | ||
| Â Â Â N-Miss | 1 | 8 | |
| Â Â Â 0 | 371 (100.0%) | 105 (63.6%) | |
| Â Â Â 1 | 0 (0.0%) | 60 (36.4%) |
Generate version of table to paste into overleaf
tab <- xtable(as.data.frame(summary(tab123456)))
print(tab, type="latex")## % latex table generated in R 4.1.1 by xtable 1.8-4 package
## % Mon Sep 27 21:42:05 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{rllll}
## \hline
## & & TD (N=372) & ASD (N=173) & p value \\
## \hline
## 1 & **Sex** & & & $<$ 0.001 \\
## 2 & \ \ \ M & 258 (69.4\%) & 148 (85.5\%) & \\
## 3 & \ \ \ F & 114 (30.6\%) & 25 (14.5\%) & \\
## 4 & **AgeAtScan** & & & 0.902 \\
## 5 & \ \ \ Mean (SD) & 10.383 (1.246) & 10.382 (1.356) & \\
## 6 & \ \ \ Range & 8.020 - 12.980 & 8.010 - 12.990 & \\
## 7 & **Race2** & & & 0.001 \\
## 8 & \ \ \ N-Miss & 0 & 1 & \\
## 9 & \ \ \ African American & 40 (10.8\%) & 11 (6.4\%) & \\
## 10 & \ \ \ Asian & 28 (7.5\%) & 3 (1.7\%) & \\
## 11 & \ \ \ Biracial & 47 (12.6\%) & 14 (8.1\%) & \\
## 12 & \ \ \ Caucasian & 257 (69.1\%) & 144 (83.7\%) & \\
## 13 & **SES.Family** & & & $<$ 0.001 \\
## 14 & \ \ \ N-Miss & 8 & 9 & \\
## 15 & \ \ \ Mean (SD) & 54.243 (9.371) & 51.396 (9.506) & \\
## 16 & \ \ \ Range & 18.500 - 66.000 & 27.000 - 66.000 & \\
## 17 & **handedness** & & & 0.259 \\
## 18 & \ \ \ N-Miss & 1 & 0 & \\
## 19 & \ \ \ Right & 333 (89.8\%) & 147 (85.0\%) & \\
## 20 & \ \ \ Left & 19 (5.1\%) & 12 (6.9\%) & \\
## 21 & \ \ \ Mixed & 19 (5.1\%) & 14 (8.1\%) & \\
## 22 & **CurrentlyOnStimulants** & & & $<$ 0.001 \\
## 23 & \ \ \ N-Miss & 1 & 8 & \\
## 24 & \ \ \ 0 & 371 (100.0\%) & 105 (63.6\%) & \\
## 25 & \ \ \ 1 & 0 (0.0\%) & 60 (36.4\%) & \\
## \hline
## \end{tabular}
## \end{table}1c. Create exclusion flowchart
1d. Proportion excluded
Define theme for proportion excluded plots
#Easier to make three separate panels and combine them than to use faceting function
My_Theme_prop = theme_light()+theme(
legend.title =element_blank(),
axis.title.x = element_text(size = 9),
axis.title.y = element_text(size = 9),
plot.title = element_text(size = 30),
axis.text.x = element_text(size = 6),
axis.text.y = element_text(size = 8),
strip.text.x = element_text(size = 10,color="black"),
strip.background = element_rect(fill="white"))1d.1 Proportion excluded in each Diagnostic Group
levels(qcMelt$Motion.Exclusion.Level) <- c("Strict", "Lenient", "None")
motion <- filter(qcMelt, Motion.Exclusion.Level!="None")
motion$Motion.Exclusion.Level <- droplevels(motion$Motion.Exclusion.Level)
levels(motion$PrimaryDiagnosis)## [1] "TD" "ASD"motion <- group_by(motion, PrimaryDiagnosis, Motion.Exclusion.Level, Included)
dx_proportions <- ggplot(motion, aes(x = PrimaryDiagnosis, fill = Included))+
geom_bar(position = "fill", alpha = .6)+
facet_grid(~Motion.Exclusion.Level)+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme_prop+theme(legend.title =element_blank())+
theme(legend.title = element_blank())+
ylab("Proportion of Children")+
theme(legend.position = "right")+
theme(legend.margin = margin(t=0, r=0, b=-1, l=-1))+
theme(legend.key.size=unit(.08, "in"))+
theme(axis.title.x = element_blank())+
theme(axis.text.x = element_text(size = 10))
png("Figures/fig_propExcludedDxOnly_cc.png",width=3,height=2,units="in",res=200)
dx_proportions
invisible(dev.off())
dx_proportions
dx_proportions <- dx_proportions +
theme(legend.position = "none") +
theme(axis.text.x = element_text(size = 6), axis.title.x = element_text(size = 9))+
xlab("Diagnosis Group")
#Pearson's chi squared tests
extib <- tibble(motion)
exNest <- extib %>%
select(c("PrimaryDiagnosis",
"Motion.Exclusion.Level", "Included")) %>%
group_by(Motion.Exclusion.Level) %>%
tidyr::nest()
#nested models
ex_chisq <- exNest %>%
mutate(stats = map(data, ~broom::tidy(chisq.test(.x$PrimaryDiagnosis, .x$Included)))) %>%
unnest(stats)
ex_chisq## # A tibble: 2 × 6
## # Groups: Motion.Exclusion.Level [2]
## Motion.Exclusion.Level data statistic p.value parameter method
## <fct> <list> <dbl> <dbl> <int> <chr>
## 1 Strict <tibble [504 × 2]> 19.9 8.07e-6 1 Pearso…
## 2 Lenient <tibble [504 × 2]> 10.3 1.30e-3 1 Pearso…A larger proportion of autistic children were excluded compared to typically developing children using both levels of motion QC.
1d.2 Proportion excluded by ADHD comorbidity in the ASD group
comorbid <- filter(motion, PrimaryDiagnosis=="ASD")
comorbid$PrimaryDiagnosis <- droplevels(comorbid$PrimaryDiagnosis)
comorbid$Diagnosis4Levels <- droplevels(comorbid$Diagnosis4Levels)
levels(comorbid$Diagnosis4Levels)## [1] "Autism" "Autism+ADHD"comorbid <- filter(comorbid, !is.na(Diagnosis4Levels))
comorbid_proportions <- ggplot(comorbid, aes(x = Diagnosis4Levels, fill = Included))+
geom_bar(position = "fill", alpha = .6)+
facet_grid(~Motion.Exclusion.Level)+
scale_x_discrete(labels = c("No", "Yes"))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme_prop+theme(legend.title =element_blank())+
xlab("Comorbid ADHD?")+
theme(legend.title = element_blank())+
#theme(legend.position="top")+
theme(legend.text = element_text(size=7))+
ylab("Proportion of Autistic Children")+
theme(legend.margin = margin(t=0, r=0, b=-1, l=-1))+
theme(legend.key.size=unit(.1, "in"))+
theme(legend.position = "none")
#theme(plot.margin = unit(c(1, 6, 1, 6), "cm"))
prop_legend = cowplot::get_legend(comorbid_proportions + guides(color = guide_legend(ncol = 1))+
theme(legend.position = "right", legend.text = element_text(size = 9),
legend.key.size=unit(.1, "in")))
asdNest <- tibble(comorbid) %>%
select(c("Diagnosis4Levels", "CurrentlyOnStimulants",
"Motion.Exclusion.Level", "Included")) %>%
reshape2::melt(id.vars = c("Motion.Exclusion.Level", "Included"),
measure.vars = c("Diagnosis4Levels", "CurrentlyOnStimulants")) %>%
group_by(Motion.Exclusion.Level, variable) %>%
tidyr::nest()## Warning: attributes are not identical across measure variables; they will be
## dropped#nested models
asd_chisq <- asdNest %>%
mutate(stats = map(data, ~broom::tidy(chisq.test(.x$value, .x$Included)))) %>%
unnest(stats)
asd_chisq## # A tibble: 4 × 7
## # Groups: Motion.Exclusion.Level, variable [4]
## Motion.Exclusion… variable data statistic p.value parameter method
## <fct> <fct> <list> <dbl> <dbl> <int> <chr>
## 1 Strict Diagnosis… <tibbl… 0.231 0.631 1 Pearson's Ch…
## 2 Lenient Diagnosis… <tibbl… 0.463 0.496 1 Pearson's Ch…
## 3 Strict Currently… <tibbl… 6.40 0.0114 1 Pearson's Ch…
## 4 Lenient Currently… <tibbl… 6.39 0.0115 1 Pearson's Ch…1d.3 Proportion excluded by Currently on stimulants in ASD group
stim <- filter(motion, PrimaryDiagnosis=="ASD")
stim$PrimaryDiagnosis <- droplevels(stim$PrimaryDiagnosis)
stim$CurrentlyOnStimulants <- factor(stim$CurrentlyOnStimulants)
levels(stim$CurrentlyOnStimulants)## [1] "0" "1"levels(stim$CurrentlyOnStimulants) = c("No", "Yes")
stim <- filter(stim, !is.na(CurrentlyOnStimulants))
stim_proportions <- ggplot(stim, aes(x = CurrentlyOnStimulants, fill = Included))+
geom_bar(position = "fill", alpha = .6)+
facet_grid(~Motion.Exclusion.Level)+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme_prop+
xlab("Currently On Stimulants?")+
theme(legend.title = element_blank())+
#theme(legend.position="top")+
theme(legend.text = element_text(size=7))+
ylab("Proportion of Autistic Children")+
theme(legend.margin = margin(t=0, r=0, b=-1, l=-1))+
theme(legend.key.size=unit(.1, "in"))+
theme(legend.position = "none")
#theme(plot.margin = unit(c(1, 6, 1, 6), "cm"))1d.4 Combine proportion plots into one figure & save
2. rs-fMRI exclusion probability as a function of age and symptom severity
Covariates
phenoVariables <- c("ID", "PrimaryDiagnosis",
"ADOS.Comparable.Total",
"SRS.Score",
"PANESS.TotalOverflowNotAccountingForAge",
"DuPaulHome.InattentionRaw",
"DuPaulHome.HyperactivityRaw",
"AgeAtScan",
"WISC.GAI",
"Motion.Exclusion.Level", "Included")
phenoIDs <- c("ID", "PrimaryDiagnosis", "Motion.Exclusion.Level", "Included")
aim1 <- reshape2::melt(qcMelt[, phenoVariables],
id.vars=names(qcMelt)[which(names(qcMelt) %in% phenoIDs)])
levels(aim1$variable) <- c("ADOS", "SRS", "Motor Overflow", "Inattention",
"Hyperactivity", "Age", "GAI")
aim1G <- group_by(aim1, PrimaryDiagnosis, Motion.Exclusion.Level, Included, variable)2a. Univarate GAMs
aim1$delta = rep(NA,length=nrow(aim1))
aim1$delta = ifelse(aim1$Included=="Included",1,0)
aim1tib <- tibble(filter(aim1, Motion.Exclusion.Level!="None"))
aim1tib$Motion.Exclusion.Level <- droplevels(aim1tib$Motion.Exclusion.Level)
aim1Nest <- aim1tib %>%
group_by(Motion.Exclusion.Level, variable) %>%
tidyr::nest()
#nested models
nested_gams <- aim1Nest %>%
mutate(model = map(data, ~mgcv::gam(1-delta~s(value, k=-1), data = na.omit(.x),
family=binomial(link=logit), method="REML")),
coefs = map(model, tidy, conf.int = FALSE),
Rsq = map_dbl(model, ~summary(.)$r.sq)) %>%
unnest(coefs)
#Ben: correct for 7 lenient and 7 strict
nested_gams_len <- nested_gams %>%
filter(Motion.Exclusion.Level=="Lenient")
nested_gams_len$p.fdr = p.adjust(nested_gams_len$p.value, method = "BH")
nested_gams_strict <- nested_gams %>%
filter(Motion.Exclusion.Level=="Strict")
nested_gams_strict$p.fdr = p.adjust(nested_gams_strict$p.value, method = "BH")
#combine to print
nested_gams <- rbind(nested_gams_len, nested_gams_strict)
#list adjusted p values
nested_gams[, c(1:2,6:11)]## # A tibble: 14 × 8
## # Groups: Motion.Exclusion.Level, variable [14]
## Motion.Exclusion.… variable edf ref.df statistic p.value Rsq p.fdr
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Lenient ADOS 2.04 2.47 18.4 2.08e-4 0.0442 4.86e-4
## 2 Lenient SRS 1.83 2.29 17.4 2.95e-4 0.0466 5.17e-4
## 3 Lenient Motor Ove… 1.00 1.00 15.6 7.58e-5 0.0308 4.54e-4
## 4 Lenient Inattenti… 1.38 1.67 7.44 1.17e-2 0.0157 1.17e-2
## 5 Lenient Hyperacti… 2.15 2.70 13.2 4.59e-3 0.0242 5.36e-3
## 6 Lenient Age 1.00 1.00 9.33 2.26e-3 0.0164 3.17e-3
## 7 Lenient GAI 1.00 1.00 14.7 1.30e-4 0.0288 4.54e-4
## 8 Strict ADOS 1.00 1.00 21.9 2.79e-6 0.0444 9.76e-6
## 9 Strict SRS 1.00 1.00 22.7 1.53e-6 0.0567 9.76e-6
## 10 Strict Motor Ove… 1.00 1.00 10.2 1.42e-3 0.0194 1.99e-3
## 11 Strict Inattenti… 1.00 1.00 17.8 2.47e-5 0.0360 4.31e-5
## 12 Strict Hyperacti… 1.88 2.35 25.0 1.30e-5 0.0516 3.04e-5
## 13 Strict Age 1.76 2.20 7.73 2.84e-2 0.0129 2.84e-2
## 14 Strict GAI 1.00 1.00 7.55 6.02e-3 0.0130 7.02e-3nested_gams <- nested_gams %>%
mutate(LB = map(data, ~round(min(na.omit(.x$value)))),
UB = map(data, ~round(max(na.omit(.x$value)))),
range = map2(LB, UB, ~seq(from=.x, to=.y, by=1)),
logpredict = map2(model, range, ~predict(.x, newdata = data.frame(value = .y), type="link",se.fit=TRUE)),
fit = map(logpredict, ~plogis(.x$fit)),
lCI = map(logpredict, ~plogis(.x$fit-1.96*.x$se.fit)),
hCI = map(logpredict, ~plogis(.x$fit+1.96*.x$se.fit)))Define theme for gam plots
2a.1 Probability of exclusion as a function of ADOS
ados <- nested_gams %>%
filter(variable=="ADOS") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_ados <- ggplot(ados, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='Probability of Exclusion', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("ADOS")+
theme(plot.title = element_text(size = 11, hjust = 0.5))2a.2 Probability of exclusion as a function of SRS
srs <- nested_gams %>%
filter(variable=="SRS") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_srs <- ggplot(srs, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("SRS")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())2a.3 Probability of exclusion as a function of Inattention
ina <- nested_gams %>%
filter(variable=="Inattention") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_in <- ggplot(ina, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("Inattention")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())2a.4 Probability of exclusion as a function of Hyperactivity
hi <- nested_gams %>%
filter(variable=="Hyperactivity") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_hi <- ggplot(hi, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("Hyperactivity")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())2a.5 Probability of exclusion as a function of Motor Overflow
mo <- nested_gams %>%
filter(variable=="Motor Overflow") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_mo <- ggplot(mo, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("Motor Overflow")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())2a.6 Probability of exclusion as a function of Age
age<- nested_gams %>%
filter(variable=="Age") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_age <- ggplot(age, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("Age")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())2a.7 Probability of exclusion as a function of GAI
gai <- nested_gams %>%
filter(variable=="GAI") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_gai <- ggplot(gai, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("GAI")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())
p_legend = cowplot::get_legend(p_gai + guides(color = guide_legend(nrow = 1))+theme(legend.position = "bottom", legend.text = element_text(size = 10), legend.key.size=unit(.1, "in")))2b. Density plots of covariates used to fit GAMs (across included & excluded children)
Define theme for density plots of covariates across included and excluded children
2b.1 ADOS density
ddata <- nested_gams %>%
filter(variable=="ADOS") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data) %>%
filter(PrimaryDiagnosis=="ASD")
ddata$PrimaryDiagnosis <- droplevels(ddata$PrimaryDiagnosis)
d_ados=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0), limits = c(0, .09), breaks=seq(0, .08, by=.02))+
labs(x='', y='Density')+
scale_fill_manual(values = c("#FDE599"))+
scale_color_manual(values = c("#E9D38D"))+
den_theme2b.2 SRS density
ddata <- nested_gams %>%
filter(variable=="SRS") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_srs=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0), limits=c(0,max(srs$range)),breaks = seq(0, 100 , by = 50))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')2b.3 Inattention density
ddata <- nested_gams %>%
filter(variable=="Inattention") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_in=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
labs(x='')+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
theme(axis.title.y = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')2b.4 Hyperactivity/Impulsivity Density
ddata <- nested_gams %>%
filter(variable=="Hyperactivity") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_hi=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')2b.5 Motor Overflow Density
ddata <- nested_gams %>%
filter(variable=="Motor Overflow") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_mo=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
theme(axis.title.y = element_blank())+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')2b.6 Age Density
ddata <- nested_gams %>%
filter(variable=="Age") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_age=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0), limits=c(8,13), breaks = seq(8, 13 , by = 1))+
scale_y_continuous(expand = c(0, 0), limits=c(0,.29), breaks=seq(0, .25, by = .05))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')2b.7 GAI Density
ddata <- nested_gams %>%
filter(variable=="GAI") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_gai=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0), limits = c(0, .035), breaks=seq(0., .03, by=.01))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
labs(x='')+
theme(axis.title.y = element_blank())
hist_legend = cowplot::get_legend(d_gai + guides(color = guide_legend(nrow = 1))+theme(legend.position = "bottom", legend.text = element_text(size = 10), legend.key.size=unit(.1, "in")))combine gam plots with densities & print
top_row <- cowplot::plot_grid(p_ados, p_srs, p_in, p_hi, p_mo, p_age, p_gai, ncol=7, rel_widths=c(1.18/7, .97/7, .97/7, .97/7, .97/7, .97/7, .97/7))
bottom_row <- cowplot::plot_grid(d_ados, d_srs, d_in, d_hi, d_mo, d_age, d_gai, ncol=7, rel_widths=c(1.18/7, .97/7, .97/7, .97/7, .97/7, .97/7, .97/7))## Warning: Removed 91 rows containing non-finite values (stat_density).## Warning: Removed 19 rows containing non-finite values (stat_density).png("Figures/fig_probExclusion_allGAM_TD_ASD_cc.png",width=10,height=6,units="in",res=200)
cowplot::plot_grid(p_legend, top_row, NULL, bottom_row, NULL, hist_legend, nrow=6, rel_heights=c(.1, 1, -.07, .5, -.07, .1))
dev.off()## quartz_off_screen
## 2#png("~/Dropbox/Apps/Overleaf/MotionSelectionBias_rsfMRI/Figures/fig_probExclusion_allGAM_TD_ASD_cc.png",width=10,height=6,units="in",res=200)
cowplot::plot_grid(p_legend, top_row, NULL, bottom_row, NULL, hist_legend, nrow=6, rel_heights=c(.1, 1, -.07, .5, -.07, .1))
#dev.off()NOTE: 19 rows = # of participants with missing Motor Overflow scores (imputed for deconfounded group difference)
3. Impact of motion QC on phenotypic representation within diagnostic groups
3a. Split violin plots
My_Theme = theme_light()+theme(
legend.title = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_text(size = 5),
axis.text.y = element_text(size = 8),
strip.text.x = element_text(size = 12, face = "bold", color="black"),
strip.text.y = element_text(size = 10, color="black"),
strip.background = element_rect(fill="white"),
plot.title = element_text(size = 9, hjust = 0.5))3a.1 ADOS (ASD only) split violin
NOTE: Missing ADOS scores for one participant evaluated at CARD
ados <- aim1 %>%
filter(PrimaryDiagnosis=="ASD" & variable=="ADOS") %>%
dplyr::select(-c("PrimaryDiagnosis", "variable"))
ados_violin <- ggplot(ados, aes(Motion.Exclusion.Level, value, fill = Included, color = Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE, adjust = 1.5) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(legend.text = element_text(size = 8))+
theme(legend.position = "none")+
ggtitle("ADOS")3a.2 SRS split violin
srs <- filter(aim1G, variable=="SRS")
aim1_srs <- ggplot(srs, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~., scales = "fixed")+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
# geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y =element_blank())+
theme(legend.position = "none")+
ggtitle("SRS")
v_legend = cowplot::get_legend(aim1_srs + guides(color = guide_legend(nrow = 2))+
theme(legend.position = "left",
legend.text = element_text(size = 10),
legend.key.size=unit(.1, "in")))## Warning: Removed 273 rows containing non-finite values (stat_ydensity).## Warning: Removed 273 rows containing non-finite values (stat_summary).
## Warning: Removed 273 rows containing non-finite values (stat_summary).NOTE: 273/3 = 91, # of participants missing SRS
with(dat3, table(PrimaryDiagnosis, is.na(SRS.Score)))##
## PrimaryDiagnosis FALSE TRUE
## TD 263 90
## ASD 150 13a.3 Inattention Split Violin
inat <- filter(aim1G, variable=="Inattention")
paper_inat <- ggplot(inat, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y = element_blank())+
theme(legend.position = "none")+
ggtitle("Inattention")3a.4 Hyperactivity/Impulsivity Spilt Violin
hyp <- filter(aim1G, variable=="Hyperactivity")
paper_hyp <- ggplot(hyp, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y = element_blank())+
theme(legend.position = "none")+
ggtitle("Hyperactivity")3a.5 Motor Overflow Split Violin
overflow <- filter(aim1G, variable=="Motor Overflow")
aim1_of <- ggplot(overflow, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y =element_blank())+
theme(legend.position = "none")+
ggtitle("Motor Overflow")3a.6 Age Split Violin
age <- filter(aim1G, variable=="Age")
aim1_age <- ggplot(age, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y =element_blank())+
theme(legend.position = "none")+
ggtitle("Age")3a.7 GAI Split Violin
gai <- filter(aim1G, variable=="GAI")
aim1_gai <- ggplot(gai, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(legend.position = "none")+
ggtitle("GAI")Combine split violins into one figure & save
3b. Mann-Whitney U tests to compare included vs excluded participants using lenient motion QC (13 tests)
#run lenient tests first
aim1tib <- tibble(filter(aim1, Motion.Exclusion.Level=="Lenient"))
aim1MW <- aim1tib %>%
group_by(PrimaryDiagnosis, variable) %>%
tidyr::nest()
#hypothesis: included children will have less severe symptoms. NOTE: ADOS only collected in ASD (9 tests)
nested_mw_less <- aim1MW %>%
filter(variable %in% c("SRS", "Inattention", "Hyperactivity",
"Motor Overflow")|(variable=="ADOS"&PrimaryDiagnosis=="ASD")) %>%
mutate(mwm = map(data, ~wilcox.test(value~Included, alternative="less", data = na.omit(.x))),
coefs = map(mwm, tidy)) %>%
unnest(coefs)
#hypothesis: included children will be older and have greater GAI (4 tests)
nested_mw_greater<- aim1MW %>%
filter(variable %in% c("Age", "GAI")) %>%
mutate(mwm = map(data, ~wilcox.test(value~Included, alternative="greater", data = na.omit(.x))),
coefs = map(mwm, tidy)) %>%
unnest(coefs)
complete_mw <- rbind(nested_mw_less, nested_mw_greater)
complete_mw$p.fdr <- p.adjust(complete_mw$p.value, method = "BH")
complete_mw[order(complete_mw$PrimaryDiagnosis, decreasing=TRUE), c(1:2, 5:9)]## # A tibble: 13 × 7
## # Groups: PrimaryDiagnosis, variable [13]
## PrimaryDiagnosis variable statistic p.value method alternative p.fdr
## <fct> <fct> <dbl> <dbl> <chr> <chr> <dbl>
## 1 ASD ADOS 1780. 0.00921 Wilcox… less 0.0264
## 2 ASD SRS 1720 0.00579 Wilcox… less 0.0264
## 3 ASD Motor Overflow 1260 0.000948 Wilcox… less 0.0123
## 4 ASD Inattention 2442. 0.642 Wilcox… less 0.642
## 5 ASD Hyperactivity 2419 0.606 Wilcox… less 0.642
## 6 ASD Age 2848 0.0216 Wilcox… greater 0.0352
## 7 ASD GAI 2960 0.00655 Wilcox… greater 0.0264
## 8 TD SRS 4562. 0.431 Wilcox… less 0.509
## 9 TD Motor Overflow 7086. 0.0569 Wilcox… less 0.0822
## 10 TD Inattention 8004 0.268 Wilcox… less 0.349
## 11 TD Hyperactivity 7018. 0.0198 Wilcox… less 0.0352
## 12 TD Age 10074. 0.0102 Wilcox… greater 0.0264
## 13 TD GAI 9882 0.0202 Wilcox… greater 0.03523c. Mann-Whitney U tests to compare included vs excluded participants using strict motion QC (13 tests)
#run lenient tests first
aim1tib <- tibble(filter(aim1, Motion.Exclusion.Level=="Strict"))
aim1MW <- aim1tib %>%
group_by(variable, PrimaryDiagnosis) %>%
tidyr::nest()
#hypothesis: included children will have less severe symptoms
nested_mw_less <- aim1MW %>%
filter(variable %in% c("SRS", "Inattention", "Hyperactivity",
"Motor Overflow")|(variable=="ADOS"&PrimaryDiagnosis=="ASD")) %>%
mutate(mwm = map(data, ~wilcox.test(value~Included, alternative="less", data = na.omit(.x))),
coefs = map(mwm, tidy)) %>%
unnest(coefs)
#hypothesis: included children will be older and have greater GAI
nested_mw_greater<- aim1MW %>%
filter(variable %in% c("Age", "GAI")) %>%
mutate(mwm = map(data, ~wilcox.test(value~Included, alternative="greater", data = na.omit(.x))),
coefs = map(mwm, tidy)) %>%
unnest(coefs)
complete_mw <- rbind(nested_mw_less, nested_mw_greater)
complete_mw$p.fdr <- p.adjust(complete_mw$p.value, method = "BH")
complete_mw[order(complete_mw$PrimaryDiagnosis, decreasing=TRUE), c(1:2, 5:9)]## # A tibble: 13 × 7
## # Groups: PrimaryDiagnosis, variable [13]
## PrimaryDiagnosis variable statistic p.value method alternative p.fdr
## <fct> <fct> <dbl> <dbl> <chr> <chr> <dbl>
## 1 ASD ADOS 1350. 0.0236 Wilcoxo… less 0.0767
## 2 ASD SRS 1312. 0.0176 Wilcoxo… less 0.0763
## 3 ASD Motor Overflow 1132. 0.0437 Wilcoxo… less 0.0847
## 4 ASD Inattention 1582 0.189 Wilcoxo… less 0.223
## 5 ASD Hyperactivity 1671 0.322 Wilcoxo… less 0.322
## 6 ASD Age 2221 0.0165 Wilcoxo… greater 0.0763
## 7 ASD GAI 1893 0.280 Wilcoxo… greater 0.303
## 8 TD SRS 7274. 0.0456 Wilcoxo… less 0.0847
## 9 TD Motor Overflow 13570. 0.149 Wilcoxo… less 0.194
## 10 TD Inattention 14006. 0.147 Wilcoxo… less 0.194
## 11 TD Hyperactivity 12199 0.00122 Wilcoxo… less 0.0159
## 12 TD Age 16656. 0.0374 Wilcoxo… greater 0.0847
## 13 TD GAI 16378. 0.0685 Wilcoxo… greater 0.1114. Edgewise functional connectivity as a function of the covariates
Combine partial correlations with melted rs-fMRI usability and covariate info
startEdgeidx = which(names(dat3)=='r.ic1.ic2')
endEdgeidx = which(names(dat3)=='r.ic29.ic30')
names(dat3)[startEdgeidx:endEdgeidx]## [1] "r.ic1.ic2" "r.ic1.ic4" "r.ic1.ic8" "r.ic1.ic13" "r.ic1.ic14"
## [6] "r.ic1.ic15" "r.ic1.ic17" "r.ic1.ic19" "r.ic1.ic21" "r.ic1.ic22"
## [11] "r.ic1.ic24" "r.ic1.ic25" "r.ic1.ic26" "r.ic1.ic27" "r.ic1.ic28"
## [16] "r.ic1.ic29" "r.ic1.ic30" "r.ic2.ic4" "r.ic2.ic8" "r.ic2.ic13"
## [21] "r.ic2.ic14" "r.ic2.ic15" "r.ic2.ic17" "r.ic2.ic19" "r.ic2.ic21"
## [26] "r.ic2.ic22" "r.ic2.ic24" "r.ic2.ic25" "r.ic2.ic26" "r.ic2.ic27"
## [31] "r.ic2.ic28" "r.ic2.ic29" "r.ic2.ic30" "r.ic4.ic8" "r.ic4.ic13"
## [36] "r.ic4.ic14" "r.ic4.ic15" "r.ic4.ic17" "r.ic4.ic19" "r.ic4.ic21"
## [41] "r.ic4.ic22" "r.ic4.ic24" "r.ic4.ic25" "r.ic4.ic26" "r.ic4.ic27"
## [46] "r.ic4.ic28" "r.ic4.ic29" "r.ic4.ic30" "r.ic8.ic13" "r.ic8.ic14"
## [51] "r.ic8.ic15" "r.ic8.ic17" "r.ic8.ic19" "r.ic8.ic21" "r.ic8.ic22"
## [56] "r.ic8.ic24" "r.ic8.ic25" "r.ic8.ic26" "r.ic8.ic27" "r.ic8.ic28"
## [61] "r.ic8.ic29" "r.ic8.ic30" "r.ic13.ic14" "r.ic13.ic15" "r.ic13.ic17"
## [66] "r.ic13.ic19" "r.ic13.ic21" "r.ic13.ic22" "r.ic13.ic24" "r.ic13.ic25"
## [71] "r.ic13.ic26" "r.ic13.ic27" "r.ic13.ic28" "r.ic13.ic29" "r.ic13.ic30"
## [76] "r.ic14.ic15" "r.ic14.ic17" "r.ic14.ic19" "r.ic14.ic21" "r.ic14.ic22"
## [81] "r.ic14.ic24" "r.ic14.ic25" "r.ic14.ic26" "r.ic14.ic27" "r.ic14.ic28"
## [86] "r.ic14.ic29" "r.ic14.ic30" "r.ic15.ic17" "r.ic15.ic19" "r.ic15.ic21"
## [91] "r.ic15.ic22" "r.ic15.ic24" "r.ic15.ic25" "r.ic15.ic26" "r.ic15.ic27"
## [96] "r.ic15.ic28" "r.ic15.ic29" "r.ic15.ic30" "r.ic17.ic19" "r.ic17.ic21"
## [101] "r.ic17.ic22" "r.ic17.ic24" "r.ic17.ic25" "r.ic17.ic26" "r.ic17.ic27"
## [106] "r.ic17.ic28" "r.ic17.ic29" "r.ic17.ic30" "r.ic19.ic21" "r.ic19.ic22"
## [111] "r.ic19.ic24" "r.ic19.ic25" "r.ic19.ic26" "r.ic19.ic27" "r.ic19.ic28"
## [116] "r.ic19.ic29" "r.ic19.ic30" "r.ic21.ic22" "r.ic21.ic24" "r.ic21.ic25"
## [121] "r.ic21.ic26" "r.ic21.ic27" "r.ic21.ic28" "r.ic21.ic29" "r.ic21.ic30"
## [126] "r.ic22.ic24" "r.ic22.ic25" "r.ic22.ic26" "r.ic22.ic27" "r.ic22.ic28"
## [131] "r.ic22.ic29" "r.ic22.ic30" "r.ic24.ic25" "r.ic24.ic26" "r.ic24.ic27"
## [136] "r.ic24.ic28" "r.ic24.ic29" "r.ic24.ic30" "r.ic25.ic26" "r.ic25.ic27"
## [141] "r.ic25.ic28" "r.ic25.ic29" "r.ic25.ic30" "r.ic26.ic27" "r.ic26.ic28"
## [146] "r.ic26.ic29" "r.ic26.ic30" "r.ic27.ic28" "r.ic27.ic29" "r.ic27.ic30"
## [151] "r.ic28.ic29" "r.ic28.ic30" "r.ic29.ic30"signalFC <- dat3[, c(1,startEdgeidx:endEdgeidx)]
dat2 <- merge(aim1, signalFC, all=TRUE, by = "ID")
fcMelt <- reshape2::melt(dat2[,1:160],
id.vars=names(dat2)[1:7],
variable.name = "edge",
value.name = "fc")4a. Run nested gams
fctib <- tibble(filter(fcMelt, Motion.Exclusion.Level!="None"))
fctib$Motion.Exclusion.Level <- droplevels(fctib$Motion.Exclusion.Level)
fcNest <- fctib %>%
filter(Included=="Included") %>%
group_by(variable, Motion.Exclusion.Level, edge) %>%
tidyr::nest()
#nested models
fc_gams <- fcNest %>%
mutate(model = map(data, ~mgcv::gam(fc~s(value), data = na.omit(.x), method="REML")),
coefs = map(model, tidy, conf.int = FALSE)) %>%
unnest(coefs)4b. Plot histograms of edgewise p-values from GAMs
Histogram of edgewise p-values for partial correlations as a function of ADOS.
NOTE: TD scores = 0
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="ADOS") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_ados_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
#scale_fill_manual(labels=c('TD','ASD'), values = c("#FDE599", "#9FB0CC"))+
#scale_color_manual(labels=c('TD','ASD'), values = c("#E9D38D", "#8C9AB4"))+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
ggtitle("ADOS")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
labs(x='p value', y='Count')+
theme(axis.title.y = element_text(size = 9))+
theme(axis.title.x = element_text(size = 7))4b.1 Histogram of edgewise p-values for partial correlations as a function of SRS
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="SRS") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_srs_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("SRS")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))4b.2 Histogram of p-values for partial correlations as a function of inattention
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="Inattention") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_in_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("Inattention")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))4b.3 Histogram of p-values for partial correlations as a function of hyperactivity
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="Hyperactivity") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_hi_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("Hyperactivity")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))4b.4 Histogram of p-values for partial correlations as a function of motor overflow
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="Motor Overflow") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_mo_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("Motor Overflow")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))4b.5 Histogram of p-values for partial correlations as a function of age
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="Age") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_age_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("Age")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))4b.6 Histogram of p-values for partial correlations as a function of GAI
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="GAI") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_gai_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("GAI")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))
hist_p_legend <- cowplot::get_legend(hist_gai_p + guides(color = guide_legend(nrow = 1))+theme(legend.position = "bottom", legend.text = element_text(size = 10), legend.key.size=unit(.1, "in"), legend.title = element_blank()))4b.7 Combine histograms and print
fc_hist <- cowplot::plot_grid(hist_ados_p, hist_srs_p, hist_in_p, hist_hi_p, hist_mo_p, hist_age_p, hist_gai_p, ncol=7, rel_widths=c(1.18/7, .97/7, .97/7, .97/7, .97/7, .97/7, .97/7))
#png("~/Dropbox/Apps/Overleaf/MotionSelectionBias_rsfMRI/Figures/fig_hist_fc.png",width=8,height=3,units="in",res=200)
png("Figures/fig_hist_rfc_facet.png",width=8,height=3,units="in",res=200)
cowplot::plot_grid(fc_hist, hist_p_legend, nrow=2, rel_heights=c(1, .1))
dev.off()## quartz_off_screen
## 2cowplot::plot_grid(fc_hist, hist_p_legend, nrow=2, rel_heights=c(1, .1))